System and method for 3D scanning

ABSTRACT

Systems and/or methods for, for a given pixel (or sub-pixel location) in an image acquired by the camera, finding which projector pixel (or more particularly, which projector column) primarily projected the light that was reflected from the object being scanned back to this camera position (e.g. what projector coordinates or projector column coordinate correspond(s) to these camera coordinates).

RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.15/890,292, filed Feb. 6, 2018, entitled SYSTEM AND METHOD FOR 3DSCANNING, which application claims the benefit of U.S. ProvisionalApplication Ser. No. 62/455,158, filed Feb. 6, 2017, entitled SYSTEM ANDMETHOD FOR 3D SCANNING, the entire disclosure of each of whichapplications are herein incorporated by reference.

FIELD OF THE INVENTION

This invention relates to systems and methods for optical 3D scanning ofobjects and handling of 3D data and image data generated by such systemsand methods.

BACKGROUND OF THE INVENTION

There is a growing need for high-accuracy, low-cost 3D scanningprocesses that can tolerate challenging conditions such as relativemotion between scanner and scanned object, non-Lambertian materials anda variety of lighting conditions.

Structured Light (SL) techniques are the best current methods foraccurate capture of 3 dimensional shapes. These are active techniquesthat illuminate objects or environments of interest with speciallydesigned patterns of visible or invisible light. Images of the objectsand/or environments are then captured with one or more cameras while thespecial patterns are illuminating the objects and/or environments. The3D geometry is calculated from the images with triangulation usingknowledge of relative angle, displacement and optical factors for thecamera and projector. The active light source allows results to berelatively invariant to different material and environmental propertiessuch as color, texture and ambient illumination. Modern light projectionengines, image sensors and digital signal processing (DSP) devicetechnology can project and capture high resolution images at high framerate reliably and accurately.

The significant qualities of the results of structured light techniquesare determined by the characteristics of the patterns (and usually thetemporal sequence of patterns) that are projected onto the object orenvironment to be captured. The purpose of the patterns is to encodeinformation that enables camera image coordinates to be directly relatedto projected image coordinates. Projected patterns typically encode theprojector image column or row coordinates so that with the use ofoptical and geometric calibration information, it becomes possible touse optical triangulation to identify 3 dimensional (3D) spacecoordinates of the object being scanned which correspond to each pixelin the projector coordinate space or sometimes each pixel in thecaptured camera images.

Structured light patterns are typically classified according to whetherthey allow retrieval of 3D coordinates corresponding to discreteprojector pixel locations or whether they allow sub-pixel (i.e.continuous) measurements. Continuous patterns may be able to find adifferent 3D coordinate for each camera pixel coordinate, or even camerasub-pixel coordinates, whereas, discrete patterns only identifypositions corresponding to discrete projector pixel coordinates. Resultsfrom discrete techniques may only have as many 3D points as projectorpixels, whereas 3D models resulting from conventional continuoustechniques may have as many 3D points as camera pixels. See, e.g., D.Moreno, W. Y. Hwang and G. Taubin. Rapid Hand Shape Reconstruction withChebyshev Phase Shifting. 2016 Fourth International Conference on 3DVision. Results from advanced techniques presented here may have camerasub-pixel resolution meaning that they may have more 3D points thancamera pixels.

Conventionally, continuous techniques require better control ofprojected colors and intensities as well as camera to projector colorand intensity correspondence and calibration of colors and intensitiesis necessary. In contrast, discrete techniques may not require thislevel of control and calibration with the downside that they may beslower and yield lower resolution.

Many continuous techniques, generally known as Phase Shifting (PS)encode a projector axis (typically the X axis of the projected image) assinusoidal grayscale or color patterns. PS techniques are more tolerantof projector defocus which is unavoidable when using large opticalapertures typical in digital projectors.

Current PS 3D scanning techniques require capturing multiple images ofan object or scene per static data set and generally make the assumptionin their algorithms that the images are of the same scene from the samevantage point. Therefore they have a requirement of little relativemotion between scanner and object or environment during the entiremultiple-image capture duration for acquisition of each individualdataset. To a certain extent the limitations of relative motion can beovercome using higher and higher frame rates, but there are directadvantages to be had in 3 dimensional accuracy, data quality andquantity and color accuracy and mapping accuracy if the number of imagesto be captured per data set can be reduced, and especially reducing therequired number of images to be captured under the influence ofnon-uniform illumination patterns.

SUMMARY OF THE INVENTION

The present invention overcomes the disadvantages of the prior art byproviding systems and/or methods for, for a given pixel (or sub-pixellocation) in an image acquired by the camera, finding which projectorpixel (or more particularly, which projector column) primarily projectedthe light that was reflected from the object being scanned back to thiscamera position (e.g. what projector coordinates or projector columncoordinate correspond(s) to these camera coordinates).

One aspect of the disclosure provides a system for capturing 3Drepresentations from an object, comprising: a light source beingconfigured to project a sequence of one or more light patterns, at leastone of the light patterns is a phase-encoded pattern containing anon-repeating pattern of gradients between each pixel and adjacentpixels, whereby the light source can project the phase-encoded patternonto a scene; a light sensor configured to capture at least one image ofthe scene under the influence of the projected pattern; and a processorconfigured to decode gradients in the captured image to determine thecoordinates in the projected image that created the light received ateach captured image pixel.

In one example, the non-repeating pattern has signs of the gradientsbetween each pixel and adjacent pixels form a non-repeating pattern.

In one example, subsets of gradients between pixels and their adjacentpixels form more than one code per pixel that each can be decoded toyield phase values.

In one example, the gradients between pixels and their adjacent pixelsare interpreted as binary or ternary numbers which are used as the basisof a phase-decoding process. In one example, pixel phase values arerecorded as valid only if more than one code decodes to the same phasevalue.

In one example, a confidence score is calculated for the decoded phasevalue assigned to a captured image pixel based on the number of decodedcodes that agree on the phase value.

Another aspect of the disclosure provides a method for capturing 3Drepresentations from an object, comprising: projecting a sequence of oneor more light patterns, at least one of the light patterns is aphase-encoded pattern containing a non-repeating pattern of gradientsbetween each pixel and adjacent pixels, whereby the light source canproject the phase-encoded pattern onto a scene; capturing at least oneimage of the scene under the influence of the projected pattern; anddecoding gradients in the captured image to determine the coordinates inthe projected image that created the light received at each capturedimage pixel.

In one example, the non-repeating pattern has signs of the gradientsbetween each pixel and adjacent pixels form a non-repeating pattern.

In one example, subsets of gradients between pixels and their adjacentpixels form more than one code per pixel that each can be decoded toyield phase values.

In one example, the gradients between pixels and their adjacent pixelsare interpreted as binary or ternary numbers which are used as the basisof a phase-decoding process.

In one example, pixel phase values are recorded as valid only if morethan one code decodes to the same phase value.

In one example, a confidence score is calculated for the decoded phasevalue assigned to a captured image pixel based on the number of decodedcodes that agree on the phase value.

Another aspect of the disclosure provides a method for generating a 3Ddataset, comprising: projecting one or more phase images onto a scene orobject; projecting one or more full-illumination images interleavedwithin the one or more phase images; capturing a first set of one ormore images of the scene or object at times when the one or more phaseimages are projected; generating a 3D dataset from the first set of oneor more images; capturing a second set of one or more images of thescene or object when the one or more full-illumination images areprojected; calculating at least one motion parameter from the second setof one or more images.

In one example, the one or more phase images have a spatial frequency inthe range of 1-200 periods per frame width.

In one example, the one or more phase images comprise a universal phaseimage.

In one example, the one or more full-illumination images are interleavedwithin the one or more phase images.

In one example, the motion parameter comprises at least one of arelative motion trajectory or an orientation difference.

In one example, capturing the first set of one or more images andcapturing the first set of one or more images are captured at differentframe rates.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention description below refers to the accompanying drawings, ofwhich:

FIG. 1 shows a Phase Shift (PS) scanner device;

FIG. 1A shows a structured light scanning process with relative motionbetween a 3D scanner and an object;

FIG. 2 shows a modified structured light scanning process;

FIGS. 3A-3C show spatial frequencies used in structured light scanningprocesses;

FIG. 3D-3U shows a set of 18 phase images that may be projected as partof a structured light 3D scanning process;

FIG. 4A is a flow chart depicting an overview of encoding, acquiring,and capturing data;

FIG. 4B is a flow chart depicting a method of encoding a scene with asingle phase-encoded image and acquiring data according to one or moreaspects of the disclosure, in which a phase-encoded image is an image inwhich one or more projector coordinate values are encoded in the pixelsof the image, and encoding may be via pixel values for brightness,color, other pixel characteristics or a combination thereof;

FIG. 4C is a flow chart depicting a method of decoding 3D point datafrom single phase-encoded scene image capture data;

FIG. 5A shows a partial image from a structured light scanning process;

FIG. 5B shows a schematic partial image from a structured light scanningprocess;

FIG. 6A shows a schematic partial image from a structured light scanningprocess.

FIG. 6B shows a schematic partial image from a structured light scanningprocess;

FIG. 6C shows a schematic partial image from a structured light scanningprocess;

FIG. 6D shows a schematic partial image from a structured light scanningprocess;

FIG. 6E shows a schematic partial image from a structured light scanningprocess;

FIG. 7 is a flow chart depicting a multi-mode 3D scanning process; and

FIG. 8 is a block diagram of computing devices that may be used toimplement the one or more systems or methods according to one or moreaspects of the disclosure.

DETAILED DESCRIPTION

“Single Frame” Depth Mapping

FIG. 1 shows a phase shift (PS) 3D scanner 102. Scanner 102 has aprojector 104 and a sensor 106. The 3D scanner 102 (also referred to asa scanner) is a device which collects depth information about itssurroundings or objects within its sensing range.

3D scanners typically use sensors and/or cameras to gather informationabout their environment. Some 3D scanners also encode their environmentwith light patterns using a light source or projector which may havepixels or discretely accessible coordinates within the image or lightpattern they project. This encoding may make it possible to determinewhich projector coordinate created light that strikes a given locationin the environment or on a particular object.

3D scanners typically use light sensors. These may be electronic sensorsthat detect light intensity and optionally color. Sensors may becharge-coupled-device (CCD) and complementary metal-oxide semiconductor(CMOS) devices or any other device that creates a signal that changesdepending upon incident light. The signal is typically electronic, butmay also be chemical or physical as in the case of conventional film.

For the purposes of this description, the term “sensor” or “lightsensor” can be taken broadly to include a light-detecting element (CCD,CMOS, etc.) that resolves images into pixels having varying grayscale orcolor (e.g. red, green, blue or cyan, magnetic, yellow) intensity valuesand any associated optics—which can be a fixed or variable lens assemblydefining an optical axis. The optical axis can be perpendicular to theimage plane of the light-detecting element or oriented at anon-perpendicular angle thereto. The optics, when variable, can be basedon a mechanical or liquid lens technology, among others and can includeauto-focus circuitry implemented according to known or customtechniques.

In one example, a 3D scanner may be embodied as a camera. The camera canhave a housing, one or more optical elements (e.g., lens, filter, etc.)for focusing or filtering light either embodied within the housing orexterior to the housing, with the sensor and processing circuitry housedwithin the housing. The camera may optionally include a display toprovide a preview of a scene to be imaged.

3D scanners may use light sources (e.g., a light emitting module). Onetype of light source is a projector. Most projectors are able tomodulate light that they emit in space and/or time. Many projectors areable to project pre-determined or dynamic images. Projectors may have afocal range or they may be focus-less (as in the case oflaser-projectors). Projectors may use visible or invisible light orother wavelengths of electromagnetic radiation. Other types of lightsources are also possible including light emitting diodes (LEDs), lightemitting screens such as LCD, OLED, etc., and incandescent bulbs.

Resolution of image sensors and cameras is typically described in termsof pixel dimensions or total number of pixels (megapixels).

Resolution of 3D scan data is typically described in terms of itsinverse, point spacing (e.g. 0.1 mm between points). A “resolution ofX.XX mm” is often employed in the art, which is, technically, the pointspacing. Sometimes a differentiation is made between the horizontalresolution and the depth resolution as these are typically different andare influenced by different factors within the same 3D scanning system.

The sensor 106 can be an overall camera assembly and/or anotherimplementation of a charge-coupled device (CCD), complementarymetal-oxide-semiconductor (CMOS), or other type of sensor arrangement.Projector 104 is configured to project appropriate structured lightpatterns (not shown) on an object being scanned 112. Projector 104 isconfigured to have a field of view (FOV) 110. FOV 110 is the angular andspatial region where light is projected. Scanning camera 106 may beconfigured to have a field of view (FOV) 114 that is narrow so that itcaptures high-resolution data from light reflected from object 112, orit may have a wide FOV to collect lower-resolution data about a largerarea.

In some examples, the scanning device 102 can include one or moresensors 106 that can capture images at differing resolutions and/orfields of view (FOV) such that additional sensors may capture furtherinformation about the environment or other objects illuminated by theprojector.

Sensor 106 can be any type of image sensor capable of capturing andimaging a scene, such as CMOS or CCD devices. The sensor 106 can beoperatively connected to one or more processor assemblies 130,respectively, to process or operate on the data received by the sensor106. In one example, the sensor 106 can be operatively connected to asingle processor assembly, while in another example multiple sensors canbe operatively connected to a separate, discrete processor assembly. Theprocessor assembly 130 main processor 132, a graphics processing unit(GPU) 134, and a memory module 136 that may store instructions forexecuting any of the processes described herein, as well as any datarelating to light patterns to be displayed by projector 104. The sensor106, alone or in combination additional sensors, can collect 3D scandata and generate a 3D model of the object.

FIG. 1A schematically shows a structured light scanning process of anobject 108 a using a scanning device 102 a (such as scanning device 102described above) having a projector 104 a that emits structured light106 a. The same pixel-coordinate location of each projected image willland on different locations of an object to be scanned if it movesrelative to the scanner during the scanning process.

FIG. 2 schematically shows a structured light scanning process whereonly one image has a pattern and the other solid-color images are motioninvariant (e.g. the color and intensity of the light on a given locationin the scene illuminated by the projected image does not changesubstantially even if there is relative motion between the scanner andthe scene for locations that remain illuminated). In this example,scanning device 202 includes a projector 204 to project light ontoobject 208. As shown, the patterns and types of light can vary overtime. At a first point in time, the light 206 a can be a solid-colorimage, such as black. At a second point in time, the light 206 b can bea uniform color (such as a uniform image of red, green, blue, cyan,magenta, yellow, etc.). At a third point in time, the light 206 c can bea stripe pattern. In one example, the stripes may be continuous straightlines, while in other examples the stripes may be discontinuous. At afourth point in time, the light 206 d an be a second uniform color,which may or may not be different from the uniform color at the secondpoint in time. The lights 206 a, 206 b, 206 c, and 206 d may beprojected in any order. In some cases some of the projected light at thevarious points in time (especially solid-color images) may be projectedmore than once within a sequence).

FIG. 3A shows pattern spatial frequencies used in a conventionalphase-shift structured light scanning process. The X axis (horizontalaxis) represents pattern frequency with low frequencies shown to theleft and high frequencies shown to the right. Spatial frequency forphase-shift scanning patterns refers to the number of pattern stripes(e.g. pattern stripe spatial periods) per unit length or per image width(e.g., the inverse of the spatial period or distance between stripes).Stripes within each image may be sinusoidal variations of intensity orthey may be other patterns of varying intensity. Images with stripepatterns are also called fringe pattern images or zebra-stripe images.Typical phase shift scanning processes project a set of pattern imageswith different spatial frequencies. The set of projected imagestypically includes a number of pattern images with low spatial frequency(in this case three are shown), plus a number of patterns (in this casethree patterns) with intermediate spatial frequency, plus a number ofpatterns with high spatial frequency (in this case four patterns).Examples of low, intermediate and high spatial frequencies are onestripe period per frame, seven stripe periods per frame and sixty-onestripe periods per frame. These frequencies can be chosen to be a widerange of values, but they work together to allow decoding (e.g.determining) which projector pixel location (or in the case of verticalstripes, which projector column) created the light that illuminates agiven pixel seen by the image sensor (e.g. camera). Each set of patternsof a given frequency essentially enables finding the projector imagecolumn location within a period (e.g. within a stripe), but it does notuniquely provide information about which projector stripe (e.g. whichperiod) within which the location is found. So the high frequenciesenable the highest precision determination of which location within somehigh frequency stripe. Then the patterns of intermediate frequencyenable decoding to which group of high frequency stripes. Then thelowest frequency patterns enable determining which broader column areaof the image the light came from. The lowest frequency patterns providepoor precision, but when combined with the intermediate and highfrequency pattern information, together they uniquely specify a preciseand accurate projector pixel column from which the light originated. Onechallenge is that low and intermediate frequency patterns are oftenclose to or overlapping with one or more “light transport frequencies”which are determined by scene or object characteristics which causelight from low or intermediate frequency patterns to be reflected orrefracted within the scene and create incorrect illumination levels assensed by a sensor (e.g. camera), and therefore incorrect depth datawhen decoded. While projector columns are discussed here with associatedvertical (or other non-horizontal) stripe patterns, other scannerconfigurations may decode projector rows instead via horizontal stripes(or other non-vertical stripe pattern).

FIG. 3B shows spatial frequencies of a typically monochrome (e.g.greyscale) white light pattern used in the Micro PS structured lightscanning process. Note that all patterns of the Micro PS processtypically use the same light color (e.g. wavelength on theelectromagnetic radiation spectrum). Micro PS is a technique that usessome number of projected image patterns (in this case 4+ are indicatedin the figure) which all have high spatial frequency. With correctlychosen pattern frequencies, projector column can be uniquely decodedwith only a variety of high frequency patterns and without requiring anylow frequency patterns. The downside is that to get good fidelity andconfidence in the data, often a large number of patterns must beprojected (four is the theoretical minimum, but in practice 12-20 areoften used). This is a challenge if the scanned object moves or ifreducing scanning time is desired.

FIG. 3C shows spatial frequencies of a multiple color light pattern usedin a modified structured light scanning process. FIG. 3C shows multiplehigh-spatial-frequency patterns of different wavelengths (e.g. colors)of light combined into a single pattern image (e.g. a singlephase-encoded image). Wavelengths may be red, green and blue light, orthey may be any other wavelengths which may be combined into a singlevisible or not-visible or mixed-visibility image.

FIG. 3D-U shows a set of 18 phase images that may be projected as partof a structured light 3D scanning process, in which the first fiveimages (FIG. 3D-3H) are low-spatial-frequency images, the second fiveimages (3I-3M) are intermediate-spatial-frequency images, and theremaining eight images (3N-3U) are high-spatial-frequency images.

The spatial frequency of images can be seen at FIGS. 3D-3U, which showlow spatial frequency images 3D-3H. In these images, there is onesinusoidal period per image width, resulting in a spatial frequency ofapproximately 1 with respect to the image width. The single period canbe seen at different phases at FIGS. 3D-3H. At FIGS. 3I-3M, it can beseen that there are approximately 24 sinusoidal periods per image widthfor an intermediate-spatial frequency image, resulting in a frequency ofapproximately 24 with respect to image width. At FIGS. 3N-3U, it can beseen that there are approximately 66 sinusoidal periods for ahigh-spatial frequency image, resulting in a frequency of approximately66 with respect to image width. The spatial frequency when these imagesare projected onto a scene depends upon how wide the image is when itstrikes the scene (e.g. how far the image spreads after it leaves theprojector). In a case where the image becomes one meter wide at areference distance in the scene, these spatial frequencies in theprojected images would become 1 per meter for FIGS. 3D-3H, 24 per meterfor FIGS. 3I-3M and 66 per meter for FIGS. 3N-3U. More generally, lowspatial frequency images can be considered in the range of approximately1 to 10, e.g., 1 to 10+/−½ per image width. Intermediate specialfrequencies can be considered in the range of approximately 10 to 40,e.g., 10 to 40+/−½ per image width. High spatial frequencies can be anyfrequency above approximately 40+/−½ per image width and up to 200 perimage width.

Conventional temporal mapping may have the disadvantage in which, ifthere exists relative motion between the scanner and the object beingscanned, the mapping may not be accurate because the same projectedfringe-pattern pixel coordinates from different captures may land ondifferent 3D locations on the object at different times thus causingerror. Therefore, it may be desirable to avoid temporal mapping byencoding all required information to extract unambiguous, accurate depthinformation into a single projected pattern (e.g. a single phase-encodedimage). It may be possible and advantageous to project other images aspart of each dataset capture cycle that are not a series of fringepatterns (e.g. are not multiple images of stripes and/or are free ofstripes) and therefore do not have the problems associated withconventional temporal mapping. For example, one or two frames ofcontinuous color+intensity could be projected before or after a singlephase-encoded pattern frame. Continuous value frames could be uniformblack, white, saturated color or any combination of color+intensity thatis uniform across the frame. The result is that as long as theseprojected continuous value frames cover the entire area for which depthis to be captured, the values retrieved during capture when these framesare projected will be invariant with moderate relative motion betweenthe capture device and the object being captured. In this way we canaccomplish a motion-invariant temporal mapping as long as the relativedisplacement between frames is below a threshold.

A specific encoding scheme: project separate frames of uniform red,green, blue, white (full illumination), black (no projectedillumination), and optionally yellow, purple and cyan (=blue+green) andother optional combinations. Then project one phase-encoded patternframe (e.g. non-constant color and/or intensity image) where pixelcolumn colors are created by combining three non-equal co-primefrequencies that are higher than the threshold frequency for globalillumination (e.g. light transport) problems (typically these must bebelow a period of about 5 mm at a distance of interest in thescene=frequency above 200/meter depending on the optical and materialcharacteristics of the scene and/or object to be scanned). Pixel colorsmay also vary across rows in a similar manner for example by choosingnon-equal co-prime frequencies for each color so that color channelvalues may vary in a continuous way along both rows and columns. Withcorrectly chosen frequencies, patterns of colors can be constructed thatdo not repeat within three-pixel by 3-pixel blocks within the image.Accurate depth information can then be calculated from the specific RGBvalues of blocks of pixels while using the values from the captures ofthe uniform frames as reference for the max and min values of the rangefor each color. It may also be possible to add even more robustness tothe values by further limiting or down-selecting the values used inconstructing the phase-encoded pattern—for example by using a low-bitdepth or using only certain combinations of light wavelengths.

Instead of constant vertical (or horizontal) stripes (i.e. whererepeating pattern of pixels is the same for each row (or column in thecase of horizontal stripes)), it is possible to encode a differentpattern in each row such that each column forms a non-repeating pattern.It is then possible to perform bit-wise operations on each sensor (e.g.camera) pixel which weight neighboring sensor pixels from rows above andbelow in the captured images, so that calculations from each pixel areable to decode or “unwrap” a specific phase of a base frequency(typically but not necessarily) corresponding to the width of theprojected images (i.e. the number of pixels in the width of theprojected image). Then the specific single pixel value is used to get anaccurate relative phase or intra-phase value. Encoding may be chosensuch that the overall phase may be recovered even when data from some ofthe adjacent captured-image pixels is of poor quality or missing due topoor capture conditions, surface discontinuities.

It may be desirable to explicitly not capture depth data for pixels thatlie immediately on or adjacent to a discontinuity or other challengingcapture condition. At first this might seem like a limitation, but ahand-held capture device with good user feedback may enable andencourage the user to move and/or change orientation to capture data inthose actual locations on the scanned object that were not usable in onecaptured dataset by capturing additional datasets from new locationsand/or orientations in which they are no longer at a discontinuity orother compromised condition in the captured dataset.

Encoding: using 3 channels (e.g. in this example R, G, B, or alternatelyany other set of sufficiently separate wavelengths such as IR, Green, UVetc) with 6 bit encoding (e.g. 64 levels for each channel) in 3locations (pixel of interest plus 2 adjacent pixels), there are 262,144total permutations (allowing repetition of colors and values). If asquare image of 360×360 pixels (by way of example) is to be projected,(note that there can be a wide range of possible image sizes andresolutions), then only 129,600 patterns are needed. This arrangement,thus provides a degree of redundancy that improves performance andreliability.

One way of encoding is to assign each row a color channel and havepixels going across the row with intensity values changing according toa sinusoid to encode relative phase. Or, it may be advantageous to use asawtooth pattern for relative phase.

Single Frame Capture for Accurate Depth Acquisition/Redundant DepthEncoding

It is desirable that, for a given pixel (or sub-pixel location) in theimage acquired by the camera, the system identifies what projector pixel(or most importantly, what projector column) primarily projects thelight that is reflected from the object being scanned back to thiscamera position (i.e. what projector coordinates or projector columncoordinate correspond(s) to these camera coordinates).

FIG. 4A is a flow chart depicting an overall method 400 of encoding 402,acquiring 404, and decoding 406 data according to one or more aspects ofthe disclosure. Encoding step 402 typically involves creating an imageto be projected by a light source (e.g. projector) in which one or morelight patterns is used to create a unique code corresponding to eachprojector pixel. Step 402 may also involve projecting the image(s) ontoan object or scene in order to complete the encoding of the object orscene. Acquiring step 404 involves capturing sensor data (e.g. capturingimages with a sensor or camera) of a scene or object under the influenceof the one or more encoded light patterns from step 402. In decodingstep 406, the images captured in step 404 are used to determine (e.g.decode or alternately look up) which projector coordinates projected thelight at each (or any relevant) sensor coordinates.

FIG. 4B is a flow chart depicting a method of encoding a scene with asingle phase-encoded image and acquiring data according to one or moreaspects of the disclosure.

At block (e.g., a “step”) 402B, compute “universal” phase-encoded imageto project (in one example this may only be done once and may bepre-computed and stored).

At block 404B, choose color channel values for each image pixel for thephase-encoded image such that inter-pixel color channel gradient signsform codes that do not repeat within the image. Alternately pixel colorchannel values (or wavelengths in the case of other areas of theelectromagnetic spectrum) may be chosen such that values of thegradients between an image pixel and adjacent pixels form the code.Using signs of gradients creates fewer possible codes but signs ofgradients may be more robust to environmental factors such asenvironmental illumination and hard-to-scan materials. Using gradientvalues allows more possible codes.

At block 406B, acquire data. This block may be a set of sub-steps, hereshown to include steps 408B to 418B.

At block 408B, project a single color image onto scene.

At block 410B, capture image of scene under influence of single-colorprojected image.

At block 412B, repeat block 408B and 410B for all (or a predeterminedsubset of) single-color images. Single color images are used toestablish a reference sensor value in pixels of captured images whichcorresponds to a known illumination intensity and wavelength.Alternately these images may be any combination of one or more referencewavelengths that is relatively invariant across the projected imagepixels.

At block 414B, project universal phase encoded image (e.g. the imagecreated in step 404B).

At block 416B, capture image of scene under influence of phase-encodedprojected image.

At block 418B, store captured images from steps 410B, 412B and 416B.

FIG. 4C is a flow chart depicting a method of decoding 3D point datafrom single phase-encoded scene image capture data.

At block 402C, evaluate captured images of scene corresponding tosingle-color projected images.

At block 404C, evaluate per-pixel brightness for each captured image.

At block 406C, store per-pixel calibration for each color-channel whichcorrelates projected brightness to captured brightness for all camerapixels for this approximate scene pose. Camera pixels that haveout-of-range values or maximum or minimum values (for example 0 or 255if pixel color channel scale is 0 to 255) may be marked as being“out-of-range” or “no-data” for the corresponding image or for anydataset derived from the corresponding image(s).

At block 408C, evaluate captured image of scene corresponding tophase-encoded projected image. This step may include sub-steps410C-416C.

At block 410C, evaluate per-pixel absolute brightness for each colorchannel.

At block 412C, use stored calibration for this pose to calculate thecorresponding approximate brightness in projected image for each colorchannel that would create the captured pixel values.

At block 414C, calculate inter-pixel gradients for each color channel ateach pixel location for the image captured under the influence of thephase-encoded projected image.

At block 416C, calculate one or more codes from inter-pixel gradients.

At decision block 418C, determine if code(s) correspond to validprojected image location (e.g. projected image pixel coordinate). Forexample if only one code is calculated, determined if that codecorresponds to a valid projected image location. Alternately, if morethan one code is calculated, determine if all codes or a majority (e.g.,greater than 50%) of codes correspond to a projected image location thatis both valid and is the same for all or a majority of the codes.

If “yes” at decision block 418, proceed to block 420C to use savedprojector-camera calibration to calculate depth (e.g. Z value) for eachcaptured image pixel via triangulation.

At block 422C, use per-pixel X and Y values from captured image pixelsplus calculated Z values to create (X,Y,Z) coordinate (or anotherappropriate coordinate system) values for each valid captured pixel.

At block 424C, set depth for pixel to unknown.

At block 426C, store all 3D data for this pose. The 3D data from thispose is a dataset and may be combined with datasets from other poses tocreate a unified, more complete 3D dataset representing an object or ascene.

The system and/or methods shown in FIGS. 4A, 4B and 4C solve thetechnological problem that relative motion between 3D scanning systems(e.g. projector, camera etc.) and scenes or objects being scannedtypically causes errors or at least reduced data accuracy. Errors orreduced accuracy can cause components manufactured on the basis ofcaptured 3D data sets to not fit, not meet required tolerances or evenmechanically fail. The inability to handle relative motion causes 3Dscanning processes to be slow, inefficient and not able to be used infor some applications where it is desired to capture 3D data. The systemand/or methods shown in FIGS. 4A, 4B and 4C solve this problem byenabling reduced error rate and/or higher data accuracy in cases whererelative motion is encountered including the use of hand-held scanningsystems and/or scanning of moving objects.

Projector coordinates can be encoded in projected images by using acombination of three (3) frequencies for R, G, B channels within eachcolumn such that that all frequencies are above the light transportfunction frequencies. Co-prime frequencies (and co-prime numbers ofintensity levels found by dividing the maximum intensity by half thepixel-period for each chosen frequency) can be selected to ensure thatthe full number of unique combinations is attained. For example, use 7intensity levels for Red, 8 for Green and 9 for Blue. Alternativelyco-prime periods can also be selected in the number of pixels to achievea similar result, for example 13, 14, and 15 pixels for Red, Green andBlue respectively. Using the first approach (7, 8, and 9 levels) yields7*8*9=504 unique RGB value combinations. These RGB values can be used asa code, where each unique combination corresponds to a unique globalphase (i.e. unique projector pixel-column correspondence from receivedcamera image to projected pixels. Interpolation of the values may evenallow sub-pixel resolution, which is generally desirable since it isoften feasible to use a camera resolution which is higher than theprojector resolution. Global phase, in turn can be directly mapped toaccurate depth information.

If it can be guaranteed that all the independent R, G, B values are readcorrectly to the level of precision required to discern between thelevels used, then the system could capture global phase accurate depthinformation with a single pattern image frame plus additionalmotion-invariant reference frames (i.e. frames that have spatiallyuniform color or illumination projected). The frequencies chosen areabove the light transport frequencies in most cases (seehttp://www.cs.columbia.edu/CAVE/projects/MicroPhaseShifting/), and usingthe uniform-color reference images/captures provides a degree ofinvariance to global illumination. See FIG. 3C. However, varyingmaterials, object colors, textures, other optical properties andsignificant slopes of the object being scanned may, in many cases, causeerrors greater than the illumination level resolution. If this occurs,for example if a blue value of 6 was projected at a projector locationbut for some reason a value of 5 was read from the corresponding camerapixel, the net reading may be off by many units in the encoding scheme(similarly to the way a tens or hundreds digit being off by 1 causes abase 10 number to jump by more than 1). These potentially large errorswould appear as significant noise or spikes in the resulting 3D data andthey would be very challenging, or impossible, to filter out.

One way to eliminate the possibility of such errors is to use redundantencoding of information. By specifically encoding absolute phase in twoor more ways per location, the results of the different encoding may becompared and thereby a much higher confidence level in their validitymay be obtained. In the cases where the different encodings do notdecode to the same or acceptably similar absolute phase or depth, thedata may be discarded or flagged for further analysis. In this way amuch more robust depth data set may be created from a single datasetconsisting of a single pattern frame (single non-motion-invariant frame)plus optional motion-invariant frames.

A method for multiple absolute phase or depth encoding: One way torobustly and redundantly encode absolute phase or depth is to encode itin local gradients. The present system and/or method can use oneencoding scheme along each row. With 7, 8 and 9 intensity levels for R,G and B respectively, there can be 504 uniquely encoded values. Eachpixel has 8 neighbors, but since the two same-row neighbors are alreadypart of a first encoding scheme, for best redundancy, it is desirable toavoid using these same-row pixels in the computation, and therefore useonly the 6 projected pixels that are adjacent in different rows.Adjacent rows can be offset by a suitable number of pixels (e.g.gradients may be calculated between pixels separated by some number ofrows, rather than between pixels in adjacent rows) so that, in mostcases, the system identifies differences when subtracting channelintensity values from one row to another—or subtracting diagonal values.The values of the results of the subtraction operations may often not beexact or accurate because of non-ideal conditions as described above. Ingeneral, however such detailed results are not required by the systemand method, as subtracting floating point, or potentially, integerintensity values would yield more information than required for anaccurate computation. The system and method can classify each gradientas positive, negative or near zero. With 3 color channels to compare and3 potential states per channel, there is a total of 3{circumflex over( )}3=27 potential combinations per pixel comparison. With 6 pixels tocompare that gives us 27{circumflex over ( )}6=387,420,489 totalpotential unique codes. This is still many more than needed so furtherrobustness can be added to error by allowing for one, two or more “bad”or indeterminate pixel comparisons. (This could happen for example ifconditions prevent acquisition of some pixel values or if the pixels arenear an edge or discontinuity). If only 3 valid pixels are employed,then there are 27{circumflex over ( )}3=19,683 unique locations to bedecoded.

Further robustness can be provided by assigning extra codes to allow fordifferent outcomes for gradients. A set of codes can be defined, createdfrom possible gradient combinations corresponding to a particularprojector pixel that include the most likely errors or inaccuracies. Thesystem and method can undertake a computation or look-up operation forall of these errors or inaccuracies, including the errors to yield thecorrect absolute phase, which can then be uniquely correlated to aunique depth value by triangulating with the camera offset.

Gradient Calculation

For each location of interest in camera pixel coordinates, the systemand method can find illumination gradients between it and locationsadjacent to it (or locations offset from it by a predetermined number ofrows, columns and/or pixels) and it can potentially find inter-pixelgradients with respect to each channel or type of sensor element in thecamera sensor array. For discussion here, we will refer to Red (R),Green (G) and Blue (B) channels (collectively RGB), but in practicethere can be other channels such as IR, UV, or any number of otherwavelengths or frequencies. The sensor elements may respond toelectromagnetic radiation (photons) or they may respond to electrons,protons or other particles or waves. If using an RGB image sensor,inter-pixel gradients can be found for each of the R, G and B channelsAND each of these can be for each adjacent pixel, or camera-coordinatelocation. To find gradient values subtract the values of each R,G and Bchannel at the location of interest from the respective R, G and Bvalues of a neighboring camera pixel (or camera coordinates in the caseof sub-pixel evaluation). The results of this subtraction operation canbe referred to as “gradient values” which may be values such as integervalues, binary values, floating point values, etc and may have a sign,or in some cases they may be very close to zero. In some cases asdescribed below it may be useful to convert these gradient values intosimpler “gradient signs”. The conversion to gradient signs can defineranges, for example any result less than −1 may be cast to a “negativegradient sign” and any result greater than 1 may be cast to a “positivegradient sign” and any result in between −1 and 1 may be cast to a “zerogradient sign” or in some cases it may be useful to cast or classifysuch results as “indeterminate gradient sign”. Other floating point orinteger values can be used as well, for example the range for casting tozero gradient to be −2.3333 to +2.3333 with negative and positive signsbeing on the outsides of that range. Or any other numbers can be chosento delineate the gradient sign conversion range. Using gradient signsinstead of numerical values for gradients makes the computation of codesrobust against potentially widely varying measured intensity values vs.intensity values under ideal conditions.

FIG. 5A shows schematic version of a portion of an image 500 to beprojected onto an object to be scanned (e.g. a block of pixels). A pixelof interest 502 is shown surrounded by neighboring pixels 504, a samepixel column 506, a right adjacent pixel column 508, a left adjacentpixel column 510, a pixel 512, a pixel 514, a pixel 516, a pixel 518, apixel 520, and a pixel 522, a pixel 524 and a pixel 526. Pixels areshown arranged in rows and columns such as a left-adjacent column 510, asame-pixel column 506, a right-adjacent column 508, an upper-adjacentrow 530, a same-pixel row 528, and a lower-adjacent row 532. Pixels 516,512 and 514 are in upper-adjacent row 530. Pixels 524 and 526 are in thesame-pixel row 528 as pixel of interest 502. Pixels 522, 518 and 520 arein lower-adjacent row 532. Pixels 516, 524 and 522 are in left-adjacentcolumn 510. Pixels 512 and 518 are in same-pixel column 506. Pixels 514,526 and 520 are in right-adjacent column 508. These example pixels maybe adjacent or they may be separated by some number of pixels (notshown) between each shown pixel.

FIG. 5B schematically shows the same pixels as FIG. 5A, with directionsfor gradients shown between them. FIG. 5B is purely schematic and onlyrepresents relative spatial position of pixels, but does not representactual spacing. Gradients 536, 538, 540, 542, 544, 546, and 548 mayrepresent changes in pixel color or intensity values in differentdirections. For example gradient 534 may represent the difference inintensity values between pixel of interest 502 and pixel 516.Furthermore, gradients 536, 538, 540, 542, 544, 546, and 548 may eachrepresent more than one gradient between their respective pixels, forexample they may each represent a gradient corresponding to each colorchannel that the projector (not shown) is capable of projecting (forexample a gradient for a red channel, a gradient for a green channel,and a gradient for a blue channel).

Pixel color and/or intensity values could be the same for the imageprojected by the projector and for the image captured by a camera aimedat an object or scene to be captured under “ideal” conditions—e.g., theobject is a flat plate with uniform optical properties and the cameraand projector fields of view are arranged so that the pixels map 1 to 1.While conditions during scanning rarely match this ideal, this is a goodplace to start for discussion, and the sign of gradients described oftenremains the same for many camera pixel locations even when images areprojected onto complex shapes.

FIGS. 6A and 6B can represent either pixels of a projected image or animage captured by a camera under ideal conditions and with goodcalibration between projector and camera. FIGS. 6A and 6B are the sameexcept FIG. 6A has labels and FIG. 6B has no labels so that items arenot obscured. FIGS. 6A and 6B show a schematic version of the pixelsfrom FIGS. 5A and 5B, with intermediate integer gradient results shownin between the pixels. Pixels 616, 612, 614, 624, 602, 626, 622, 618 and620 correspond to pixels 516, 512, 514, 524, 502, 526, 522, 518 and 520respectively. FIGS. 6A-6E are purely schematic and only representrelative spatial positions of pixels, but do not represent actualspacing. Extra room has been provided in the figures to showintermediate gradient results. In real images gradient results may beheld in computer memory and are not actually represented directly in theimages. Pixel 602 is shown as having a blue channel value 650, a greenchannel value 652 and a red channel value 654. All pixels may havecorresponding color channel values. Rows 1,12 and 23 have red channelvalues, rows 2, 13, and 24 have green channel values and rows 3, 14 and25 have blue channel values. Gradients of color or intensity values maybe calculated in any direction between any two coordinate valuesprovided there is a way to obtain the color channel or intensity valuesat the points. In FIGS. 6A and 6B gradients are shown calculated betweendiscrete pixels, but this need not necessarily be the case. It may alsobe possible to evaluate or obtain color or intensity values in-betweenpixels. Gradient directions 634, 636, 638, 646, 644 and 642 are shownwhich correspond to gradients calculated from coordinates of pixel 602to coordinates of neighboring pixels. Each gradient labeled here maycorrespond to separate gradient values for each color or intensitychannel, for example gradient direction 636 may have values that arecalculated by subtracting the intensity values at coordinates for pixel602 from the intensity values for coordinates of Pixel 612 for eachrespective channel. In this specific example a red component of gradient636 is calculated by subtracting a red value 654 of 4 for pixel 602 fromthe red value 656 of 2 for pixel 612 yielding a red component ofgradient 636 with a value of −2 which means that the red intensitydeclines by a value of 2 as we move the distance and direction betweenpixel 602 and pixel 612. Similar calculations may be made for greenchannel 652 and blue channel 650 for pixel 602. Similar calculations maybe made for red green and blue channels between any two sets ofcoordinates. In FIGS. 6A and 6B, gradients from pixel 602 are only showncalculated in directions 634, 636, 638, 646, 644 and 642. Whilegradients could be calculated from pixel 602 for all 8 neighboringpixels (or for any other pixels), we have chosen to use these 6 gradientdirections in this example as these gradients yield enough informationto encode and decode locations even in the presence of errors.

FIG. 6C shows the same pixels from FIGS. 6A and 6B, but instead ofshowing the integer gradient values, gradient components have beenconverted to “signs” (e.g. positive, negative or no sign which arerepresented as 1, −1 or 0 respectively which may be used as ternaryvalues). Alternatively, signs could also be taken as either positive ornegative (without a “no sign state”) which could then be used as binaryvalues. Then “location codes” have been computed using the gradientsigns around each pixel. The location codes correspond to coordinates inthe projected image, so location codes that are computed from a cameraimage during 3D scanning may be used to find the corresponding projectorimage location which in turn may be triangulated to yield a unique depthvalue. There are many ways that location codes can be computed. We havechosen to illustrate just one of the many possibilities here. Pixel 602c is a location of interest, with Red, Green and Blue channel intensityvalues similar to FIGS. 6A and 6B. Gradient sign values 606 c may beternary values computed from gradient values (such as those in FIGS. 6Aand 6B) as described below. Location codes 606 c may be generated basedon gradient sign values 604 c.

One version of gradient signs and one version of codes that may becomputed is by using ternary sign values for RGB gradients over 6neighboring pixels as ternary numbers. The codes shown are large numbersbecause gradients from 6 neighboring pixels are used to create thecodes. The codes would have a smaller maximum range if only 4 or 3 ofthe neighboring pixels were used to create the codes. The gradient signsin this figure were computed using an effective range of gradient valueswithin which to assign “zero gradient” value of −1 to 1. Negativegradient signs are shown as “−1”. Zero or undefined gradient signs areshown as “0” and positive gradient signs are shown as “1”. For example,if a gradient component value was −2 or less, its corresponding gradientsign here is negative and is shown as −1. If a gradient component valuewas −1, it is decided to have a “zero gradient” and its gradient sign isshown here as 0. If a gradient component value was 1, it is decided tohave a “zero gradient” and its gradient sign is shown here as 0. If agradient component value was 2 or greater, its corresponding gradientsign here is positive and is shown as 1. Gradient codes 606 c weregenerated here by using the gradient sign as a ternary number (i.e. 0, 1or 2) where a negative gradient sign represents a 0, a zero gradientsign represents a 1 and a positive gradient sign represents a 2.Gradient signs from the 3 channels for each of the six gradientdirections were used as 18 “digits” of ternary numbers (similar tobinary numbers, but ternary numbers are base 3 instead of base 2). Codes606 c are shown here converted to base 10 numbers since they aregenerally easier for humans to think about, but these can be representedas base 3 numbers or any other base desired and they can be used thesame way as location codes.

FIG. 6D shows another version of gradient signs. The pixels andgradients in FIG. 6D are the same as FIG. 6C except that a differentrange for “zero gradient” has been used. The gradient signs in thisfigure were computed using an effective range within which to assign“zero gradient” value of −2 to 2. More of the gradient signs aretherefore zero in this version. Either of these ways or many otherschemes can be used to compute gradients or gradient signs or locationcodes.

FIG. 6E shows an alternate version of the same pixels and gradients fromFIG. 6D, but with some changes to some pixel red, green, blue (R,G,B)channel values to demonstrate the influence of imperfect, real-worldconditions that we might encounter in camera images capturing intensitydata when one of the ideal structured light patterns described here(such as if the pattern of FIG. 6B) was projected onto an object to bescanned. In FIG. 6E all items are the same as FIG. 6D except whereotherwise noted and highlighted. Pixel channel values 602 e((row,column) locations (1,J), (2,J), (3, J), (1,M), (2, M), (1, P), (2,P), (3,P) have been adjusted to be lower than their ideal values,simulating a condition where lower intensities are captured for an areacompared to the ideal values. Pixel channel values 604 e ((row, column)locations (1, V), (2, V), (3, V), (1, Y), (2,Y), (3, Y), (2, AB), (3,AB) have been adjusted upward to simulate an area where more light iscaptured than in the ideal case. Pixel channel values 606 e ((Row,column) locations (1, AE), (2, AE), (3, AE), (1, AH), (2, AH), (3, AH),(1, AK), (2, AK), (3, AK) have been adjusted to zero (e.g. “0”) tosimulate an area where no data is captured for some reason. Areas ofgradient sign results that would be affected by these pixel intensityvalues are shown highlighted white rectangular shaped groups labeled 608e, 610 e and 612 e. In many cases such as gradient signs 608 e, thechanged channel intensities do not affect the resulting gradient signsand therefore the resulting location codes are also unaffected. All thegradient signs of gradient signs group 608 e are not affected by thechanged channel intensities as compared to the same gradients (e.g. samerow, column elements) in FIG. 6D. In another rectangular group ofgradient signs 610 e, only a single gradient sign at (row, column)location (11,V) was affected. Specifically, the gradient sign atlocation (11,V) was a “0” in FIG. 6D and became a “1” in FIG. 6E. Allthe other gradient signs in gradient signs group 610 e are unaffected inFIG. 6E despite the changed (e.g. imperfect) intensity values ascompared with the ideal intensity values and resulting gradient signs ofFIG. 6D. The one changed gradient sign at location (11,V) does changethe location code computed, but for small changes such as this, likelyalternative codes can be tracked for each projector location, or anencoding scheme can be used in which fewer sets of gradients are used tocreate each code, so there are redundant codes. Using one of theseapproaches, the location is still likely able to be decoded in thissituation. Another rectangular group of gradient signs 612 e related tothe pixels 606 e that were altered to have zero intensity values showsmore gradient signs that are changed compared to the ideal case. (Row,column) locations (10, AE), (11, AE), (10, AH), (11, AH), and (11, AK)all have gradient values that differ compared to the ideal gradientresults of FIG. 6D. In this case, the location codes won't match anyprojector location (or multiple redundant codes found for this pixelwon't match each other) and the data may optionally be discarded and adepth value may optionally not be recorded or may be recorded as a “nodata” indicator.

Because more codes are available than are needed to perform thecomputations of the system and method, codes can be generated thatcorrespond to different bounds for sign selection, for example, thesystem and method can generate codes using “zero sign” range from −1 to1, and generate more codes using a zero sign range from −2 to 2.

Each (or a predetermined subset, e.g., at least some) of these codes fora given camera pixel location can correspond to the same phase value andtherefore depth. This method provides redundancy and allowsdetermination of the correct phase result despite variations inconditions that create differences in light intensity received at camerapixels under actual conditions vs. light received under idealconditions.

If the system and method converts gradient values to gradient signs,then for each R,G, and B channel and for each adjacent pixel there arethree (3) possible values (+, −, or 0) and these can be treated asternary numbers (base 3) for each channel. Optionally, the system maynot use the 0 signs (i.e. classifying them as indeterminate) and may useonly the positive (e.g. +) and negative (e.g. −) values and interpretthem as binary values, for example by using “+” as a binary 1 value and“−” as a binary 0 value. For the purpose of the present example, assumethere exist base-3 values. So for each of the three chosen pixel pairs(for example pixels of interest 510 to pixel 512) there are three base-3numbers which allow for representation of 27 unique values (e.g. 0 to26). This is because there are 3{circumflex over ( )}3=27 possiblecombinations. If data is used from 3 adjacent pixels, there are27{circumflex over ( )}3=19,683 unique combinations or “codes” that canbe represented. These may each correspond to a unique global phase (e.g.horizontal projector coordinate), although in many cases they may notall be used. Note that in some examples it may be desirable to only usethe six immediately adjacent pixels that are not in the same row. Whileit may be possible to use all eight immediately adjacent pixels, theones in the same row may be used to independently encode local phase andmay therefore have generally small gradients of intensity between them.If the six adjacent pixels are used there are (6×5×4)/(3×2×1) ways tochoose three of the six to create codes from the gradients. There arepotentially 20 different codes that can be checked for any pixel ofinterest that should all agree in the ideal case. Most of the 20 choiceshave some overlap (i.e. they share one (1) or more pixels whichgradients are found from), but the codes are different even if somepixels overlap so they still provide a sufficient level of independence.The system and method can also optionally compare two sets of completelyindependent codes by choosing a second code that uses none of the pixelsused in creating the first code.

The above-described techniques and associated computations can beaccomplished with simple, fast, bit-wise or bit-shift operations and canoperate on values in matrix or vector form which can be much faster thansequential or loop-based calculations. These operations can also becoded so as to be performed on commercially available or customizedgraphics processing unit (GPU) chips or systems which employ otherparallel-processing.

Gradients may be invariant to sub-pixel coordinates: The pixels in animage captured by the camera will usually not be perfectly aligned ormapped with the projector pixels. There are several reasons for this.First, the camera resolution may in general be higher than that of theprojector, so a given projector pixel may project light that is receivedby several camera pixels. Second, projector images generally shift bynon-integer camera pixel amounts when they reflect off of displacedobject surfaces. Third, calibration may result in non-perfect pixelcorrespondence. In many cases sub-pixel correspondence is of interestand it is not expected to find the center of a camera pixel perfectlyaligned with or corresponding to the center of a projector pixel. Thegradient encoding method described here can be invariant to sub-pixelmisalignment up to a threshold. The threshold is generally up to onehalf of a projector pixel. Misalignment of the center of camera pixel Ato projector pixel B above one half the width of a projector pixelcauses camera pixel A to correspond to the next projector pixel over inthe direction of the shift (call it projector pixel C). To put itanother way, the sign of the gradients found between a pixel of interestand its adjacent pixels if the pixel centers are aligned will often bethe same as the sign of the gradients found when the center of thecamera pixel is shifted up to one half projector pixel width withrespect to the same projector pixel. This is generally true even morefrequently if the shift is up to one half of a camera pixel width (incases where the camera pixels are smaller than the projector pixels(e.g., camera has higher resolution per corresponding scanned objectarea than the projector, which may often be the case)). This property ofinvariance of sign of the gradients to sub-pixel displacements meansthat there is great robustness for obtaining absolute phase at camerapixel resolution. Additionally or alternatively, the present system ormethod can look at actual slope values to get sub-pixel information orinterpolate between pixels if desired and these potentiallyfiner-resolution data points can be double-checked against the robustpixel-resolution data. As long as gradient values associated with theideal projected image vary continuously (as they do in some of themethods and systems described here), gradients found via evaluatingcamera pixel color channel and/or intensity values may be used todetermine sub-pixel projector phase and/or coordinates. Redundant setsof camera pixel gradients or codes calculated from pixel gradients maybe statistically analyzed to yield probabilities of differentsub-projector-pixel locations or a probability distribution of locationin projector-image coordinates. Inter-pixel correction factors can alsobe applied that correct for the effect of large “blocky” projectorpixels vs. smaller camera pixels. Specifically, the present systemand/or method can pre-test what camera pixel RGB values are foundcorresponding to different sub-projector-pixel locations under idealconditions and calibrate and/or create correction factors accordingly.This sub-pixel calibration correction can prevent “jaggy” artifacts thatmight otherwise appear in the depth data related to the difference indiagonal length to horizontal or vertical length of projector pixels.

The system and method can ensure that each (or at least somepredetermined subset) code only maps to a single absolute phase value orprojector coordinate value. The remaining potential for error is simplythat if the pixel RGB values in the image captured by the camera havesignificant error, causing many or all of the gradients to have largeerrors compared with their ideal encoded values in the projected image,then a code can be generated from gradients that is not intended tocorrespond with the pixel it is generated from. In some applications itmay be acceptable to have some errors or noise. But in manyapplications, errors are either not acceptable or must be greatlyminimized.

After acquiring a dataset, “error-checked” absolute phase can be foundand therefore depth values corresponding to each camera pixel can befound as follows: Use a single code generated from gradients betweenthis pixel and surrounding pixels to find an absolute phase value. Ifenough mapped codes exist including likely gradient errors, it is likelythat a valid absolute phase value will result. Another approach that mayensure a valid result, is to encode absolute phase using only a subsetof the inter-pixel gradients (for example create codes using only 3 or 4of the inter-pixel gradients). In this scenario, many different codeswill be mapped to the same absolute phase value. These can be found, andthen the absolute phase can be found redundantly using 2 or more codesgenerated from the same camera pixel being investigated. If theresulting absolute phase result is the same, the result can be kept witha high confidence that it is correct. If the result is not the same,additional different codes can be generated from this pixel and theircorresponding absolute phase values may be checked also. If afterchecking the desired maximum number of codes there is no much match, orif the desired number of matches is not achieved, either a) the data canbe discarded from this pixel or b) the data can be flagged as suspect.In some applications it may be very acceptable or desirable to quicklydiscard data from a pixel if a match is not found or confidence is nothigh enough. Using this method, there exists great control over both thecomputational resources expended to find an absolute phase data pointAND also the confidence level in the data that is kept. By requiring amatch between 2 or more (or potentially many) absolute phase values fromindependently encoded gradients each corresponding to the same pixel ofinterest, substantially 100% confidence (e.g., at least 95%) canachieved that the correct absolute phase has been recorded correspondingto this camera pixel and therefore that the correct absolute depth hasbeen recorded for this location on the object being scanned to theresolution limit afforded by the constraints of the camera, projectorand calibration. Stated another way, depth data can be generated withsubstantially no noise (e.g., 0% to 1% noise). This is distinctlydifferent from and a distinct advantage over current scanning techniqueswhich generate results that almost always have “noise” or somemeasurable or noticeable errors in the data vs. ground truth. This noisein typical scanning techniques typically limits the practical uses ofthe data and/or requires extensive extra steps and/or human effort toclean, cull and refine the data to make it suitable for a particularapplication.

In some examples, it may be desirable to capture higher qualityinformation in limited areas (e.g., give up capture in some areas) thanto capture complete depth information for all pixels. In this regard, auser or the system can choose some or even a majority of the projectorpixels that can be used purely to encode additional information (or evenblack-out to avoid ambiguity) for specific other pixel locationscorresponding where depth is to be computed. Then depth data for otherpixel locations can be captured subsequently to create the desireddata-density (i.e. resolution) and coverage. This can be done either bychanging the projector pixels which are depth locations vs. auxiliarylocations in the projected image(s) and capturing data from the same (ornearly the same) device location/orientation OR by keeping the sameinformation encoding and projected image(s) and intentionally using themotion of the capture device so that capture locations fall on differentplaces on the object being captured over time. Rapid frame-rate capturemay aid in the practicality of both of these approaches.

It may also be useful to “abandon” depth values from calculations thatdo not meet a certain threshold for validity or confidence

One aspect of the present disclosure provides a method of projectingdifferent patterns or types of illumination at different times duringthe capturing of one or more images of a scene.

Multi-Mode Capture Sequence or Process

In one example, a projector can project intermittent bursts of zebrastripes frames (e.g. phase images) to capture depth data sets with oneor more full illumination visual photo frames (or movie frames) inbetween.

Full illumination photo frames can be used to calculate motiontrajectory between the intermittent bursts of zebra stripe frames (e.g.,depth data sets capture events), where motion trajectory can be a simpledelta (position, orientation) between depth data sets, or it can alsoinclude position, orientation and 1st, 2nd, 3rd and 4th (and higher)derivatives of position and orientation with respect to time. Where 1stderivatives=velocity and angular velocity, 2nd derivatives=accelerationand angular acceleration, 3rd derivatives=jerk and angular jerk etc.

Bursts of zebra stripes frames may be captured at a rate that is higherthan the rate of “photo frames”. For example, the sensor can capture ascene at a first frame rate (for example 120 frames per second) that isgreater than a frame rate of capture during projection of the fullillumination frames (for example 30 frames per second).

Bursts of zebra stripes frames may be treated as approximately static intime, meaning that for most practical purposes, relative motion betweenthe scene and the scanning system would be below a required threshold,for example 0.1 mm. In this regard, the system may be able to neglectposition differences between zebra stripes frames.

The sequence of projecting different patterns (e.g., zebra stripe, fullillumination, etc.) can be highly variable. In one example, it may bedesirable to order the sequence to avoid strobing effect, and insteadcreate the appearance of a more “random” sequence. This can create amore random perception rather than sequential. In this regard, thesystem can add additional photo full light frames between phase imagesto decrease visual strobing effect for eyes from white light.

FIG. 7 is a flowchart describing a multi-mode capture sequence orprocess 702. At block 704, the process starts. At block 706, project aset of one or more phase images with a projector onto a scene or object.Phase images projected at block 706 may be for the phase images of FIGS.3D-3U with spatial frequencies between 1 and 200 periods per imagewidth, or they may be a universal phase image created according to FIGS.5A, 5B and 6A-6E with multiple embedded frequencies above 40 periods perimage width. At block 708, optionally project one or morefull-illumination images interleaved with the projected phase images. Atblock 710, capture camera images of the scene or object at timescorresponding with each phase image to create a set of camera images. Atblock 712, store camera images for later decoding or decode a real-time3D dataset from camera images. At block, project one or more fullillumination or single-color image(s) onto the scene or object with aprojector. At block 716, with a camera (e.g., sensor), capture one ormore full illumination mages of the scene or object at time(s)corresponding to the projected full illumination images. At block 718,calculate a relative motion trajectory and/or orientation differencebased on the captured full illumination images. At block, 720 repeatsteps 706-718 as needed to capture more 3D data sets with correspondingmotion trajectories. At block, 722 the process is done.

The process shown in FIG. 7 may be a solution to two technologicalproblems: One technological problem is that individual 3D data setscaptured as part of 3D scanning processes with 3D scanning hardware maybe mis-aligned or difficult to align correctly and a resulting unified3D data set may contain some data that does not accurately represent theshape of the object being scanned. This may result in componentsmanufactured from these 3D datasets being rejected, not fitting properlyor not meeting required tolerances. The process shown in FIG. 7 maysolve this by providing motion trajectories or orientation differenceswith times (e.g. timestamps) corresponding to respective 3D data setswhich may be used to correctly align the 3D datasets to create a unified3D data set in which all data correctly represents the object beingscanned and no data is mis-aligned. A second technological problem isthat humans may have adverse reactions such as nausea, headache and orvisual distress when performing scanning processes that project rapidsequences zebra stripe images (e.g. phase images). The process of FIG. 7may reduce or eliminate human adverse reactions by providing interleavedfull-illumination frames resulting in the appearance of a more-constantor less “strobe-like” overall illumination experience for the user.

FIG. 8 is a block diagram of computing devices 2000, 2050 that may beused to implement the systems and methods described in this document, aseither a client or as a server or plurality of servers. Computing device2000 is intended to represent various forms of digital computers, suchas laptops, desktops, workstations, personal digital assistants,servers, blade servers, mainframes, and other appropriate computers.Computing device 2050 is intended to represent various forms of mobiledevices, such as personal digital assistants, cellular telephones,smartphones, and other similar computing devices. Additionally,computing device 2000 or 2050 can include Universal Serial Bus (USB)flash drives. The USB flash drives may store operating systems and otherapplications. The USB flash drives can include input/output components,such as a wireless transmitter or USB connector that may be insertedinto a USB port of another computing device. The components shown here,their connections and relationships, and their functions, are meant tobe exemplary only, and are not meant to limit implementations of theinventions described and/or claimed in this document.

Computing device 2000 includes a processor 2002, memory 2004, a storagedevice 2006, a high-speed interface 2008 connecting to memory 2004 andhigh speed expansion ports 2010, and a low speed interface 2012connecting to low speed bus 2014 and storage device 2006. Each of thecomponents 2002, 2004, 2006, 2008, 2010, and 2012, are interconnectedusing various busses, and may be mounted on a common motherboard or inother manners as appropriate. The processor 2002 can processinstructions for execution within the computing device 2000, includinginstructions stored in the memory 2004 or on the storage device 2006 todisplay graphical information for a GUI on an external input/outputdevice, such as display 2016 coupled to high speed interface 2008. Inother implementations, multiple processors and/or multiple buses may beused, as appropriate, along with multiple memories and types of memory.Also, multiple computing devices 2000 may be connected, with each deviceproviding portions of the necessary operations (e.g., as a server bank,a group of blade servers, or a multi-processor system).

The memory 2004 stores information within the computing device 2000. Inone implementation, the memory 2004 is a volatile memory unit or units.In another implementation, the memory 2004 is a non-volatile memory unitor units. The memory 2004 may also be another form of computer-readablemedium, such as a magnetic or optical disk.

The storage device 2006 is capable of providing mass storage for thecomputing device 2000. In one implementation, the storage device 2006may be or contain a computer-readable medium, such as a floppy diskdevice, a hard disk device, an optical disk device, or a tape device, aflash memory or other similar solid state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations. A computer program product can be tangibly embodied inan information carrier. The computer program product may also containinstructions that, when executed, perform one or more methods, such asthose described above. The information carrier is a computer- ormachine-readable medium, such as the memory 2004, the storage device2006, or memory on processor 2002.

The high-speed controller 2008 manages bandwidth-intensive operationsfor the computing device 2000, while the low speed controller 2012manages lower bandwidth-intensive operations. Such allocation offunctions is exemplary only. In one implementation, the high-speedcontroller 2008 is coupled to memory 2004, display 2016 (e.g., through agraphics processor or accelerator), and to high speed expansion ports2010, which may accept various expansion cards (not shown). In theimplementation, low speed controller 2012 is coupled to storage device2006 and low speed expansion port 2014. The low speed expansion port,which may include various communication ports (e.g., USB, Bluetooth,Ethernet, wireless Ethernet) may be coupled to one or more input/outputdevices, such as a keyboard, a pointing device, a scanner, or anetworking device such as a switch or router, e.g., through a networkadapter.

The computing device 2000 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as astandard server 2020, or multiple times in a group of such servers. Itmay also be implemented as part of a rack server system 2024. Inaddition, it may be implemented in a personal computer such as a laptopcomputer 2022. Alternatively, components from computing device 2000 maybe combined with other components in a mobile device (not shown), suchas device 2050. Each of such devices may contain one or more ofcomputing device 2000, 2050, and an entire system may be made up ofmultiple computing devices 2000, 2050 communicating with each other.

Computing device 2050 includes a processor 2052, memory 2064, aninput/output device such as a display 2054, a communication interface2066, and a transceiver 2068, among other components. The device 2050may also be provided with a storage device, such as a microdrive orother device, to provide additional storage. Each of the components2050, 2052, 2064, 2054, 2066, and 2068, are interconnected using variousbuses, and several of the components may be mounted on a commonmotherboard or in other manners as appropriate.

The processor 2052 can execute instructions within the computing device2050, including instructions stored in the memory 2064. The processormay be implemented as a chipset of chips that include separate andmultiple analog and digital processors. Additionally, the processor maybe implemented using any of a number of architectures. For example, theprocessor 2002 may be a FPGA, ASIC, CISC (Complex Instruction SetComputers) processor, a RISC (Reduced Instruction Set Computer)processor, or a MISC (Minimal Instruction Set Computer) processor. Theprocessor may provide, for example, for coordination of the othercomponents of the device 2050, such as control of user interfaces,applications run by device 2050, and wireless communication by device2050.

Processor 2052 may communicate with a user through control interface2058 and display interface 2056 coupled to a display 2054. The display2054 may be, for example, a TFT (Thin-Film-Transistor Liquid CrystalDisplay) display or an OLED (Organic Light Emitting Diode) display, orother appropriate display technology. The display interface 2056 maycomprise appropriate circuitry for driving the display 2054 to presentgraphical and other information to a user. The control interface 2058may receive commands from a user and convert them for submission to theprocessor 2052. In addition, an external interface 2062 may be providedin communication with processor 2052, so as to enable near areacommunication of device 2050 with other devices. External interface 2062may provide, for example, for wired communication in someimplementations, or for wireless communication in other implementations,and multiple interfaces may also be used.

The memory 2064 stores information within the computing device 2050. Thememory 2064 can be implemented as one or more of a non-transitorycomputer-readable medium or media, a volatile memory unit or units, or anon-volatile memory unit or units. Expansion memory 2074 may also beprovided and connected to device 2050 through expansion interface 2072,which may include, for example, a SIMM (Single In Line Memory Module)card interface. Such expansion memory 2074 may provide extra storagespace for device 2050, or may also store applications or otherinformation for device 2050. Specifically, expansion memory 2074 mayinclude instructions to carry out or supplement the processes describedabove, and may include secure information also. Thus, for example,expansion memory 2074 may be provided as a security module for device2050, and may be programmed with instructions that permit secure use ofdevice 2050. In addition, secure applications may be provided via theSIMM cards, along with additional information, such as placingidentifying information on the SIMM card in a non-hackable manner.

The memory may include, for example, flash memory and/or NVRAM memory,as discussed below. In one implementation, a computer program product istangibly embodied in an information carrier. The computer programproduct contains instructions that, when executed, perform one or moremethods, such as those described above. The information carrier is acomputer- or machine-readable medium, such as the memory 2064, expansionmemory 2074, or memory on processor 2052 that may be received, forexample, over transceiver 2068 or external interface 2062.

Device 2050 may communicate wirelessly through communication interface2066, which may include digital signal processing circuitry wherenecessary. Communication interface 2066 may provide for communicationsunder various modes or protocols, such as GSM voice calls, SMS, EMS, orMMS messaging, CDMA, TDMA, PDC, WCDMA, CDMA2000, or GPRS, among others.Such communication may occur, for example, through radio-frequencytransceiver 2068. In addition, short-range communication may occur, suchas using a Bluetooth, WiFi, or other such transceiver (not shown). Inaddition, GPS (Global Positioning System) receiver module 2070 mayprovide additional navigation- and location-related wireless data todevice 2050, which may be used as appropriate by applications running ondevice 2050.

Device 2050 may also communicate audibly using audio codec 2060, whichmay receive spoken information from a user and convert it to usabledigital information. Audio codec 2060 may likewise generate audiblesound for a user, such as through a speaker, e.g., in a handset ofdevice 2050. Such sound may include sound from voice telephone calls,may include recorded sound (e.g., voice messages, music files, etc.) andmay also include sound generated by applications operating on device2050.

The computing device 2050 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as acellular telephone 2080. It may also be implemented as part of asmartphone 2082, personal digital assistant, or other similar mobiledevice.

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof.These various implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms “machine-readable medium”“computer-readable medium” refers to any computer program product,apparatus and/or device (e.g., magnetic discs, optical disks, memory,Programmable Logic Devices (PLDs)) used to provide machine instructionsand/or data to a programmable processor, including a machine-readablemedium that receives machine instructions as a machine-readable signal.The term “machine-readable signal” refers to any signal used to providemachine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device(e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor)for displaying information to the user and a keyboard and a pointingdevice (e.g., a mouse or a trackball) by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback); and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in acomputing system that includes a back end component (e.g., as a dataserver), or that includes a middleware component (e.g., an applicationserver), or that includes a front end component (e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the systems and techniquesdescribed here), or any combination of such back end, middleware, orfront end components. The components of the system can be interconnectedby any form or medium of digital data communication (e.g., acommunication network). Examples of communication networks include alocal area network (“LAN”), a wide area network (“WAN”), peer-to-peernetworks (having ad-hoc or static members), grid computinginfrastructures, and the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

This document describes technologies that can be applied to a wide rangeof uses, which are designed and intended to be beneficial for all usersinvolved. However, some of the technologies described may be put toillegitimate, malicious, and even illegal ends by bad actors. This istrue with almost any technology, but there is often a greatersensitivity when a technology interacts with a user's security andprivate information. The described technologies all are designed tooperate in an environment and in a manner that respects the rights ofall users. As such, features such as user notification, opt-in andopt-out procedures, and privacy settings are available options to beused to ensure user security and privacy are respected.

Although a few implementations have been described in detail above,other modifications are possible. In addition, the logic flows depictedin the figures do not require the particular order shown, or sequentialorder, to achieve desirable results. Other steps may be provided, orsteps may be eliminated, from the described flows, and other componentsmay be added to, or removed from, the described systems. Accordingly,other implementations are within the scope of the following claims.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments of the apparatus and method of the presentinvention, what has been described herein is merely illustrative of theapplication of the principles of the present invention. As used hereinvarious directional and orientational terms (and grammatical variationsthereof) such as “vertical”, “horizontal”, “up”, “down”, “bottom”,“top”, “side”, “front”, “rear”, “left”, “right”, “forward”, “rearward”,and the like, are used only as relative conventions and not as absoluteorientations with respect to a fixed coordinate system, such as theacting direction of gravity. Moreover, a depicted process or processorcan be combined with other processes and/or processors or divided intovarious sub-processes or processors. Such sub-processes and/orsub-processors can be variously combined according to embodimentsherein. Likewise, it is expressly contemplated that any function,process and/or processor herein can be implemented using electronichardware, software consisting of a non-transitory computer-readablemedium of program instructions, or a combination of hardware andsoftware. Accordingly, this description is meant to be taken only by wayof example, and not to otherwise limit the scope of this invention.

What is claimed is:
 1. A method for generating a 3D dataset, comprising:projecting one or more phase images onto a scene or object; projectingone or more full-illumination images; capturing a first set of one ormore images of the scene or object at times when the one or more phaseimages are projected; generating a 3D dataset from the first set of oneor more images; generating a timestamp corresponding to the 3D dataset,wherein the timestamp corresponds to the relative motion trajectory ofthe scene or object between the one or more phase images; capturing asecond set of one or more images of the scene or object when the one ormore full-illumination images are projected; calculating at least onemotion parameter from the second set of one or more images.
 2. Themethod of claim 1, wherein the one or more phase images have a spatialfrequency in the range of 1-200 periods per frame width.
 3. The methodof claim 1, wherein the one or more phase images comprise a universalphase image.
 4. The method of claim 1, wherein the one or morefull-illumination images are interleaved within the one or more phaseimages.
 5. The method of claim 1, wherein the motion parameter comprisesat least one of a relative motion trajectory or an orientationdifference.
 6. The method of claim 1, wherein capturing the first set ofone or more images and capturing the first set of one or more images arecaptured at different frame rates.
 7. The method of claim 1, wherein theat least one motion parameter comprises 1^(st) and 2^(n) derivatives ofposition and orientation with respect in time.
 8. The method of claim 1,wherein the at least one motion parameter further comprises 3^(rd) and4^(th) derivatives of position and orientation with respect to time. 9.The method of claim 1, wherein the one or more phases images and the oneor more full-illumination images are projected in a sequence to avoid astrobing effect.
 10. The method of claim 1, wherein the one or morephase images comprise spatial frequencies between 1 and 200 periods perimage width.
 11. The method of claim 1, wherein the one or morefull-illumination images are interleaved with the one or more phaseimages.
 12. A method for generating a 3D dataset, comprising: projectingat least a first plurality of phase images and a second plurality ofphase images onto a scene or object; projecting, between the firstplurality of phase images and the second plurality of phase images, oneor more full-illumination images onto the scene or object; capturing afirst set of one or more images of the scene or object at times when atleast one of the first plurality of phase images or the secod pluralityof phase images are projected; generating a 3D dataset from the firstset of one or more images; capturing a second set of one or more imagesof the scene or object when the one or more full-illumination images areprojected; calculating at least one motion parameter from the second setof one or more images, wherein the motion parameter comprises a relativemotion trajectory of the scene or object between the first plurality ofphase images and the second plurality of phase images.
 13. The method ofclaim 12, further comprising generating a timestamp corresponding to the3D dataset, wherein the timestamp corresponds to the relative motiontrajectory of the scene or object between the first plurality of phaseimages and the second plurality of phase images.
 14. A method forgenerating a 3D dataset, comprising: projecting at least a firstplurality of phase images and a second plurality of phase images onto ascene or object; projecting, between the first plurality of phase imagesand the second plurality of phase images, one or more full-illuminationimages onto the scene or object; capturing a first set of one or moreimages of the scene or object at time when at least one of the firstplurality of phase images or the second plurality of phase images areprojected; generating a 3D dataset from the first set of one or moreimages; capturing a second set of one or more images of the scene orobject when the one or more full-illumination images are projected;calculating at least one motion parameter from the second set of one ormore images, wherein the motion parameter comprises an orientationdifference of the scene or object of the object between the firstplurality of phase images and the second plurality of phase images. 15.The method of claim 14, further comprising generating a timestampcorresponding to the 3D dataset, wherein the timestamp corresponds tothe orientation difference of the scene or object between the firstplurality of phase images and the second plurality of phase images. 16.A method for generating a 3D dataset, comprising: projecting one or morephase images onto a scene or object; projecting one or morefull-illumination images; capturing a first set of one or more images ofthe scene or object at times when the one or more phase images areprojected; generating a 3D dataset from the first set of one or moreimages; generating a timestamp corresponding to the 3D dataset, whereinthe timestamp corresponds to the orientation difference of the scene orobject between the one or more phase images; capturing a second set ofone or more images of the scene or object when the one or morefull-illumination images are projected; calculating at least one motionparameter from the second set of one or more images.
 17. A method forgenerating a 3D dataset, comprising: projecting one or more phase imagesonto a scene or object, wherein additional full light frames are addedbetween the one or more phase images to decrease a visual strobingeffect; projecting one or more full-illumination images; capturing afirst set of one or more images of the scene or object at times when theone or more phase images are projected; generating a 3D dataset from thefirst set of one or more images; capturing a second set of one or moreimages of the scene or object when the one or more full-illuminationimages are projected; calculating at least one motion parameter from thesecond set of one or more images.